Harnessing the Combined Power of Theoretical and Experimental Data through Multiscale Informatics
Harnessing the Combined Power of Theoretical and Experimental Data through Multiscale Informatics
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DOI：
10.1002/kin.20984
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发表时间：
2016-04
影响因子：
1.5
通讯作者：
M. P. Burke
中科院分区：
文献类型：
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作者：
M. P. Burke
从头算理论动力学计算能力的巨大的、最近的和迅速持续的改进使我们有理由相信，化学动力学领域的进步可以通过理论在动力学建模中的作用及其与实验的关系的相应演变而加速。本文回顾并提供了额外的证明，当在一个单一的数学框架内平等地考虑跨多个尺度的理论和实验数据时，包括两者的相关不确定性，会产生独特的优势。也就是说，多尺度信息学框架同时集成了来自各种来源和尺度的信息：从头开始计算分子性质的电子结构，单个反应的速率常数确定，以及测量的多反应系统的全局可观测值。由此产生的模型表示由一组理论动力学参数（具有受限的不确定性）组成，这些参数通过基本动力学模型与速率常数（具有传播的不确定性）相关，而速率常数又通过物理模型与全局可观测值（具有传播的不确定性）相关。概述了方法和典型的实现，并简要讨论了理论动力学计算、复杂化学机制的动力学模型和实验的物理模型中的主要不确定性（参数和结构）。提倡在各个方面实现更高水平的自动化，包括闭环自主混合实验和计算模型改进，以促进方法的可扩展性，以合理的人力和计算成本应用于更大的系统。通过一系列实例说明了跨多尺度理论和实验数据相结合的独特优势。本文简要总结了以前的结果，证明了理论和实验数据的同声传译在评估复杂系统的一致性和可靠的、基于物理的有限数据外推方面的效用。提出了新的结果，以证明多尺度知情模型对小（分子性质）和大（全球可观测）尺度的高预测精度。这些新结果提供了优化产生物理现实参数调整的例子，以及实验中物理模型不确定性大于动力学模型不确定性的例子。新的结果也展示了多尺度信息学方法在实验和理论计算设计中的效用，考虑了理论和实验现有知识以及解释潜在新数据时相关的参数和结构不确定性。这些新的结果提供了在实验设计中忽略结构不确定性导致无法确定最值得的实验的例子。化学动力学领域的进一步发展（特别是在理论、动力学建模和实验的交叉领域）将受益于对这三者的参数和结构不确定性的更多关注——模型参数之间的不确定性大小和交叉相关性，以及模型结构本身的局限性。
Monumental, recent and rapidly continuing, improvements in the capabilities of ab initio theoretical kinetics calculations provides reason to believe that progress in the field of chemical kinetics can be accelerated through a corresponding evolution of the role of theory in kinetic modeling and its relationship with experiment. The present article reviews and provides additional demonstrations of the unique advantages that arise when theoretical and experimental data across multiple scales are considered on equal footing, including the relevant uncertainties of both, within a single mathematical framework. Namely, the multiscale informatics framework simultaneously integrates information from a wide variety of sources and scales: ab initio electronic structure calculations of molecular properties, rate constant determinations for individual reactions, and measured global observables of multireaction systems. The resulting model representation consists of a set of theoretical kinetics parameters (with constrained uncertainties) that are related through elementary kinetics models to rate constants (with propagated uncertainties) that in turn are related through physical models to global observables (with propagated uncertainties). An overview of the approach and typical implementation is provided along with a brief discussion of the major uncertainties (parametric and structural) in theoretical kinetics calculations, kinetic models for complex chemical mechanisms, and physical models for experiments. Higher levels of automation in all aspects, including closed‐loop autonomous mixed‐experimental‐and‐computational model improvement, are advocated for facilitating scalability of the approach to larger systems with reasonable human effort and computational cost. The unique advantages of combining theoretical and experimental data across multiple scales are illustrated through a series of examples. Previous results demonstrating the utility of simultaneous interpretation of theoretical and experimental data for assessing consistency in complex systems and for reliable, physics‐based extrapolation of limited data are briefly summarized. New results are presented to demonstrate the high predictive accuracy of multiscale informed models for both small (molecular properties) and large (global observables) scales. These new results provide examples where the optimization yields physically realistic parameter adjustments and where physical model uncertainties in experiments are larger than kinetic model uncertainties. New results are also presented to demonstrate the utility of the multiscale informatics approach for design of experiments and theoretical calculations, accounting for both theoretical and experimental existing knowledge as well as relevant parametric and structural uncertainties in interpreting potential new data. These new results provide examples where neglecting structural uncertainties in design of experiments results in failure to identify the most worthwhile experiment. Further progress in the chemical kinetics field (particularly at the intersection of theory, kinetic modeling, and experiment) would benefit from increased attention to understanding parametric and structural uncertainties for all three—the uncertainty magnitude and cross‐correlations among model parameters as well as limitations of the model structures themselves.